Remote sensing image semantic segmentation method based on regional attention multi-scale feature fusion

Abstract

The invention provides a remote sensing image semantic segmentation method based on regional attention multi-scale feature fusion. The remote sensing image semantic segmentation method comprises the following steps of: S1, constructing a network model for a remote sensing image semantic segmentation network; S2, constructing a training data set, and preprocessing the collected data set for training; and S3, inputting the data set for training into a network model for training, and predicting a result after acquiring training parameters. According to the remote sensing image semantic segmentation method, the idea of an image cascade network is introduced, and the model parameter quantity is greatly reduced; meanwhile, coding features and decoding features are optimized by using an attentionmechanism, a regional attention module and a multi-scale group fusion module are constructed, feature maps of different scales are extracted and fused, training is guided by using multi-scale semantic tags and boundary tags, and the model performance is effectively improved under the condition that the parameter quantity of the model is only 8.4 M.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a remote sensing image semantic segmentation method based on multi-scale feature fusion of regional attention. Background technique [0002] With the advancement of science and technology, remote sensing satellite technology has been developed in the long run, and the research on semantic segmentation of remote sensing images has also become hot. However, remote sensing images have the characteristics of complex imaging, redundant information, and various types. Therefore, how to improve remote sensing images The segmentation accuracy has become a focus of many researchers. The classification process of traditional methods can be divided into two steps: feature extraction and object classification. That is, the feature extraction algorithm is used to extract the target features, and then the classifier is used to classify the target. Common feature extraction methods g...

AI Technical Summary

Problems solved by technology

However, traditional algorithms still face the problems of limited sample space, cumbersome and time-consuming, and poor nonlinear ability.

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